In today's society, end users regularly utilize network and other service providers to gain access to the Internet, access software services, request and receive various types of content, place internet-based calls, and perform a variety of other tasks and functions. A company that has a large number of end users or that offers highly popular content often utilizes content distribution networks and systems to serve the company's content to such end users. This is especially true when high performance, reliability, scalability, and cost savings associated with distributing the content are important to the company. Content distribution networks often provide a wide variety of advantages to companies that utilize such networks. For example, content distribution networks offer a high level of performance because content is often cached on edge servers, and content requests from end users are typically directed to the closest or most optimal node in the content distribution network. Additionally, the company's various content assets are dynamically distributed across multiple servers in such a way that an outage in one region does not substantially affect the content distribution network's ability to continue providing content to end users. Furthermore, content distribution networks typically provide a large number of servers and other devices for delivering content, and, as a result, are more readily able to handle unpredictable surges in end users or requests for content.
Content distribution networks typically deploy server farms at peering points, and provide their services to various business customers using these same server farms. In such a model, identifying specific traffic flows that are associated with a particular business customer of the content distribution network is often difficult. This is particularly true when a single server serves many of the content distribution network's customers simultaneously. In such a scenario, packet headers may appear to be the same for all customers that are served by the server of the content distribution network. This makes it difficult to provide differentiated services to various traffic flows of customers in current content distribution networks. Currently, in order to identify a specific traffic flow associated with a specific customer, devices such as Deep Packet Inspection (DPI) devices are deployed to monitor requests for content, such as HTTP GET requests or other similar requests. As the DPI devices monitor requests for content, the DPI devices then create memory state to associate a particular monitored request to a specific flow that follows the request. Such operations performed by DPI devices, proxy devices, network address translation devices, or other similar devices are often memory intensive and highly complex. In order to incorporate such functionality, the number of devices in the content distribution network, the amount of resources spent on the content distribution network, and the complexity of the content distribution network often have to be increased substantially.